Medical imaging such as MRI and CT is playing a crucial role for daily image-based diagnosis in Radiology. The images are currently visually evaluated by trained physicians and medical decisions are being made by subjective judgments. Currently computational supports for image reading are used only for limited tissue areas and a vast majority of the images are evaluated without computational supports.
When physicians evaluate anatomy, they have an ability to dynamically control the level of anatomical granularity they are inspecting. This disclosure is based on our discovery that this dynamic granularity control is the reason why past computational support could never approach human's ability to comprehend anatomy and accurately detect abnormalities in patients.
For example, when a radiologist is reading an MR image of a dementia patient, the doctor first can evaluate the overall brain atrophy. In this case, the size of the entire hemisphere and the ventricles are evaluated. The brainstem and the cerebellum size could also be evaluated as a clue for hemispheric-specific atrophy or to rule out the involvement of the cerebellum. Then the overall status of the cortex, the white matter, and the deep gray matter structures are evaluated. The inspection continues to smaller granularity levels, in which atrophy of each lobes and specific gray matter nuclei are evaluated. For example, the involvement of only the temporal lobe could indicate a specific disease class. Intensity abnormalities in the white matter could also indicate diffuse axonal injuries. The granularity level of the visual inspection could also increase substantially when the doctor is seeking for a certain type of small anatomical signatures; such as the volume loss of the caudate in the Huntington's disease or intensity abnormality in the pons for a certain type of ataxia.
This type of dynamic granularity control has never been implemented and deployed in the computational diagnosis supports in the past. For quantitative image analysis, the highest granularity level, which is one voxel, has been historically used. This means, every voxel is measured and tested for an existence of the abnormality. As being the smallest unit of imaging, the voxel-based analysis carries the maximum amount of anatomical information and in theory it is capable of detecting any type of abnormalities; thus evaluation with lower granularity levels are not necessary. This type of analysis, however, fails to replace human judgment; a human does not evaluate images in voxel levels. Voxel-based analysis is widely used for quantitative analysis of brain MRI. While it provides the highest granularity level of spatial information (i.e. each voxel), the sheer number of the voxels and noisy information from each voxel often leads to low sensitivity for abnormality detection. Thus, the primary reason of the failure is that information from each voxel is noisy and there are too many voxels.
To ameliorate this problem, spatial filtering, which effectively makes the voxel size larger, has been used, leading to decreased granularity levels. However, as granularity is reduced, information may also be lost. As another means of ameliorating this issue, it is common to introduce granularity reduction by applying isotropic spatial filtering. However, again, this type of isotropic reduction of the image granularity level is not what human does; they control granularity based on anatomy.
What is needed are methods, systems, and media that analyze anatomy from multiple granularity levels.